tools.py

'''Some helper functions for PyTorch.'''
import os
import sys
import time
import math
import numpy as np
import torch
import torch.nn as nn

def get_sub_image(mega_image,overlap=0.2,ratio=1,crop_size=512):
    #mage_image: original image
    #ratio: ratio * 512 counter the different heights of image taken
    #return: list of sub image and list fo the upper left corner of sub image
    coor_list = []
    sub_image_list = []
    w,h,c = mega_image.shape
    if w < crop_size or h < crop_size:
        mega_image = image_padding(mega_image)
    size  = int(ratio*crop_size)
    num_rows = int(w/int(size*(1-overlap)))
    num_cols = int(h/int(size*(1-overlap)))
    new_size = int(size*(1-overlap))
    for i in range(num_rows+1):
        if (i == num_rows):
            for j in range(num_cols+1):
                if (j==num_cols):
                    sub_image = mega_image[-size:,-size:,:]
                    coor_list.append([w-size,h-size])
                    sub_image_list.append (sub_image)
                else:
                    sub_image = mega_image[-size:,new_size*j:new_size*j+size,:]
                    coor_list.append([w-size,new_size*j])
                    sub_image_list.append (sub_image)
        else:
            for j in range(num_cols+1):
                if (j==num_cols):
                    sub_image = mega_image[new_size*i:new_size*i+size,-size:,:]
                    coor_list.append([new_size*i,h-size])
                    sub_image_list.append (sub_image)
                else:
                    sub_image = mega_image[new_size*i:new_size*i+size,new_size*j:new_size*j+size,:]
                    coor_list.append([new_size*i,new_size*j])
                    sub_image_list.append (sub_image)
    return sub_image_list,coor_list

def image_padding(mega_image):
    w,h,c = mega_image.shape
    result = np.full((max(512,h),max(512,w), 3), (0,0,0), dtype=np.uint8)
    result[0:h,0:w] = mega_image
    return result

def py_cpu_nms(dets, thresh):  
    """Pure Python NMS baseline.""" 
    dets = np.asarray(dets) 
    x1 = dets[:, 0]  
    y1 = dets[:, 1]  
    x2 = dets[:, 2]  
    y2 = dets[:, 3]  
    scores = dets[:, 4] 
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)  
    order = scores.argsort()[::-1]  
 
    keep = []  
    while order.size > 0:  
        i = order[0]  
        keep.append(i)  
        xx1 = np.maximum(x1[i], x1[order[1:]])  
        yy1 = np.maximum(y1[i], y1[order[1:]])  
        xx2 = np.minimum(x2[i], x2[order[1:]])  
        yy2 = np.minimum(y2[i], y2[order[1:]])  
  
        w = np.maximum(0.0, xx2 - xx1 + 1)  
        h = np.maximum(0.0, yy2 - yy1 + 1)  
        inter = w * h  
        ovr = inter / (areas[i] + areas[order[1:]] - inter)  
        inds = np.where(ovr <= thresh)[0]  
        order = order[inds + 1]  
    return keep

def sort_key(row):
    return row[-1]

def filter_small_fp(bbox_list):  
    """Remove small predictions"""
    bbox_area_list = []
    new_bbox_list = []
    bbox_list.sort(key = sort_key,reverse=True)
    for bbox in bbox_list[0:max(int(0.05*len(bbox_list)),1)]:
        bbox_area_list.append((bbox[2]-bbox[0])*(bbox[3]-bbox[1]))
    print(len(bbox_area_list))
    average_area = np.mean(bbox_area_list)

    for bbox in bbox_list:
        bbox_area = (bbox[2]-bbox[0])*(bbox[3]-bbox[1])
        if abs(bbox_area-average_area)/average_area < 0.8:
            new_bbox_list.append(bbox)

    return new_bbox_list




def get_mean_and_std(dataset, max_load=10000):
    '''Compute the mean and std value of dataset.'''
    # dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)
    mean = torch.zeros(3)
    std = torch.zeros(3)
    print('==> Computing mean and std..')
    N = min(max_load, len(dataset))
    for i in range(N):
        print(i)
        im,_,_ = dataset.load(1)
        for j in range(3):
            mean[j] += im[:,j,:,:].mean()
            std[j] += im[:,j,:,:].std()
    mean.div_(N)
    std.div_(N)
    return mean, std

def mask_select(input, mask, dim=0):
    '''Select tensor rows/cols using a mask tensor.

    Args:
      input: (tensor) input tensor, sized [N,M].
      mask: (tensor) mask tensor, sized [N,] or [M,].
      dim: (tensor) mask dim.

    Returns:
      (tensor) selected rows/cols.

    Example:
    >>> a = torch.randn(4,2)
    >>> a
    -0.3462 -0.6930
     0.4560 -0.7459
    -0.1289 -0.9955
     1.7454  1.9787
    [torch.FloatTensor of size 4x2]
    >>> i = a[:,0] > 0
    >>> i
    0
    1
    0
    1
    [torch.ByteTensor of size 4]
    >>> masked_select(a, i, 0)
    0.4560 -0.7459
    1.7454  1.9787
    [torch.FloatTensor of size 2x2]
    '''
    index = mask.nonzero().squeeze(1)
    return input.index_select(dim, index)

def meshgrid(x, y, row_major=True):
    '''Return meshgrid in range x & y.

    Args:
      x: (int) first dim range.
      y: (int) second dim range.
      row_major: (bool) row major or column major.

    Returns:
      (tensor) meshgrid, sized [x*y,2]

    Example:
    >> meshgrid(3,2)
    0  0
    1  0
    2  0
    0  1
    1  1
    2  1
    [torch.FloatTensor of size 6x2]

    >> meshgrid(3,2,row_major=False)
    0  0
    0  1
    0  2
    1  0
    1  1
    1  2
    [torch.FloatTensor of size 6x2]
    '''
    a = torch.arange(0,x)
    b = torch.arange(0,y)
    xx = a.repeat(y).view(-1,1)
    yy = b.view(-1,1).repeat(1,x).view(-1,1)
    return torch.cat([xx,yy],1) if row_major else torch.cat([yy,xx],1)

def change_box_order(boxes, order):
    '''Change box order between (xmin,ymin,xmax,ymax) and (xcenter,ycenter,width,height).

    Args:
      boxes: (tensor) bounding boxes, sized [N,4].
      order: (str) either 'xyxy2xywh' or 'xywh2xyxy'.

    Returns:
      (tensor) converted bounding boxes, sized [N,4].
    '''
    assert order in ['xyxy2xywh','xywh2xyxy']
    a = boxes[:,:2]
    b = boxes[:,2:]
    if order == 'xyxy2xywh':
        return torch.cat([(a+b)/2,b-a+1], 1)
    return torch.cat([a-b/2,a+b/2], 1)

def box_iou(box1, box2, order='xyxy'):
    '''Compute the intersection over union of two set of boxes.

    The default box order is (xmin, ymin, xmax, ymax).

    Args:
      box1: (tensor) bounding boxes, sized [N,4].
      box2: (tensor) bounding boxes, sized [M,4].
      order: (str) box order, either 'xyxy' or 'xywh'.

    Return:
      (tensor) iou, sized [N,M].

    Reference:
      https://github.com/chainer/chainercv/blob/master/chainercv/utils/bbox/bbox_iou.py
    '''
    if order == 'xywh':
        box1 = change_box_order(box1, 'xywh2xyxy')
        box2 = change_box_order(box2, 'xywh2xyxy')

    N = box1.size(0)
    M = box2.size(0)

    lt = torch.max(box1[:,None,:2], box2[:,:2])  # [N,M,2]
    rb = torch.min(box1[:,None,2:], box2[:,2:])  # [N,M,2]

    wh = (rb-lt+1).clamp(min=0)      # [N,M,2]
    inter = wh[:,:,0] * wh[:,:,1]  # [N,M]

    area1 = (box1[:,2]-box1[:,0]+1) * (box1[:,3]-box1[:,1]+1)  # [N,]
    area2 = (box2[:,2]-box2[:,0]+1) * (box2[:,3]-box2[:,1]+1)  # [M,]
    iou = inter / (area1[:,None] + area2 - inter)
    return iou

def box_nms(bboxes, scores, threshold=0.5, mode='union'):
    '''Non maximum suppression.

    Args:
      bboxes: (tensor) bounding boxes, sized [N,4].
      scores: (tensor) bbox scores, sized [N,].
      threshold: (float) overlap threshold.
      mode: (str) 'union' or 'min'.

    Returns:
      keep: (tensor) selected indices.

    Reference:
      https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/py_cpu_nms.py
    '''
    #print (bboxes.shape,scores.shape)
    if (len(bboxes.shape)==1):
        bboxes = bboxes.unsqueeze(0)
    x1 = bboxes[:,0]
    y1 = bboxes[:,1]
    x2 = bboxes[:,2]
    y2 = bboxes[:,3]

    areas = (x2-x1+1) * (y2-y1+1)
    _, order = scores.sort(0, descending=True)

    keep = []
    while order.numel() > 0:
        if order.numel() == 1:
            i = order.item()
        else:
            i = order.data[0]

        keep.append(i)

        if order.numel() == 1:
            break

        xx1 = x1[order[1:]].clamp(min=x1[i])
        yy1 = y1[order[1:]].clamp(min=y1[i])
        xx2 = x2[order[1:]].clamp(max=x2[i])
        yy2 = y2[order[1:]].clamp(max=y2[i])

        w = (xx2-xx1+1).clamp(min=0)
        h = (yy2-yy1+1).clamp(min=0)
        inter = w*h

        if mode == 'union':
            ovr = inter / (areas[i] + areas[order[1:]] - inter)
        elif mode == 'min':
            ovr = inter / areas[order[1:]].clamp(max=areas[i])
        else:
            raise TypeError('Unknown nms mode: %s.' % mode)

        ids = (ovr<=threshold).nonzero().squeeze()
        if ids.numel() == 0:
            break
        order = order[ids+1]
    return torch.LongTensor(keep)

def softmax(x):
    '''Softmax along a specific dimension.

    Args:
      x: (tensor) input tensor, sized [N,D].

    Returns:
      (tensor) softmaxed tensor, sized [N,D].
    '''
    xmax, _ = x.max(1)
    x_shift = x - xmax.view(-1,1)
    x_exp = x_shift.exp()
    return x_exp / x_exp.sum(1).view(-1,1)

def one_hot_embedding(labels, num_classes):
    '''Embedding labels to one-hot form.

    Args:
      labels: (LongTensor) class labels, sized [N,].
      num_classes: (int) number of classes.

    Returns:
      (tensor) encoded labels, sized [N,#classes].
    '''
    y = torch.eye(num_classes)  # [D,D]
    return y[labels]            # [N,D]

def msr_init(net):
    '''Initialize layer parameters.'''
    for layer in net:
        if type(layer) == nn.Conv2d:
            n = layer.kernel_size[0]*layer.kernel_size[1]*layer.out_channels
            layer.weight.data.normal_(0, math.sqrt(2./n))
            layer.bias.data.zero_()
        elif type(layer) == nn.BatchNorm2d:
            layer.weight.data.fill_(1)
            layer.bias.data.zero_()
        elif type(layer) == nn.Linear:
            layer.bias.data.zero_()

#_, term_width = os.popen('stty size', 'r').read().split()
term_width = 80
TOTAL_BAR_LENGTH = 86.
last_time = time.time()
begin_time = last_time
def progress_bar(current, total, msg=None):
    global last_time, begin_time
    if current == 0:
        begin_time = time.time()  # Reset for new bar.

    cur_len = int(TOTAL_BAR_LENGTH*current/total)
    rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1

    sys.stdout.write(' [')
    for i in range(cur_len):
        sys.stdout.write('=')
    sys.stdout.write('>')
    for i in range(rest_len):
        sys.stdout.write('.')
    sys.stdout.write(']')

    cur_time = time.time()
    step_time = cur_time - last_time
    last_time = cur_time
    tot_time = cur_time - begin_time

    L = []
    L.append('  Step: %s' % format_time(step_time))
    L.append(' | Tot: %s' % format_time(tot_time))
    if msg:
        L.append(' | ' + msg)

    msg = ''.join(L)
    sys.stdout.write(msg)
    for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):
        sys.stdout.write(' ')

    # Go back to the center of the bar.
    for i in range(term_width-int(TOTAL_BAR_LENGTH/2)):
        sys.stdout.write('\b')
    sys.stdout.write(' %d/%d ' % (current+1, total))

    if current < total-1:
        sys.stdout.write('\r')
    else:
        sys.stdout.write('\n')
    sys.stdout.flush()

def format_time(seconds):
    days = int(seconds / 3600/24)
    seconds = seconds - days*3600*24
    hours = int(seconds / 3600)
    seconds = seconds - hours*3600
    minutes = int(seconds / 60)
    seconds = seconds - minutes*60
    secondsf = int(seconds)
    seconds = seconds - secondsf
    millis = int(seconds*1000)

    f = ''
    i = 1
    if days > 0:
        f += str(days) + 'D'
        i += 1
    if hours > 0 and i <= 2:
        f += str(hours) + 'h'
        i += 1
    if minutes > 0 and i <= 2:
        f += str(minutes) + 'm'
        i += 1
    if secondsf > 0 and i <= 2:
        f += str(secondsf) + 's'
        i += 1
    if millis > 0 and i <= 2:
        f += str(millis) + 'ms'
        i += 1
    if f == '':
        f = '0ms'
    return f